Correlating subjective user states with objective occurrences associated with a user

ABSTRACT

A computationally implemented method includes, but is not limited to: acquiring subjective user state data including at least a first subjective user state and a second subjective user state; acquiring objective context data including at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user; and correlating the subjective user state data with the objective context data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.

SUMMARY

A computationally implemented method includes, but is not limited to:acquiring subjective user state data including at least a firstsubjective user state and a second subjective user state; acquiringobjective context data including at least a first context dataindicative of a first objective occurrence associated with a user and asecond context data indicative of a second objective occurrenceassociated with the user; and correlating the subjective user state datawith the objective context data. In addition to the foregoing, othermethod aspects are described in the claims, drawings, and text forming apart of the present disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

A computationally implemented system includes, but is not limited to:means for acquiring subjective user state data including at least afirst subjective user state and a second subjective user state; meansfor acquiring objective context data including at least a first contextdata indicative of a first objective occurrence associated with a userand a second context data indicative of a second objective occurrenceassociated with the user; and means for correlating the subjective userstate data with the objective context data. In addition to theforegoing, other system aspects are described in the claims, drawings,and text forming a part of the present disclosure.

A computationally implemented system includes, but is not limited to:circuitry for acquiring subjective user state data including at least afirst subjective user state and a second subjective user state;circuitry for acquiring objective context data including at least afirst context data indicative of a first objective occurrence associatedwith a user and a second context data indicative of a second objectiveoccurrence associated with the user; and circuitry for correlating thesubjective user state data with the objective context data. In additionto the foregoing, other system aspects are described in the claims,drawings, and text forming a part of the present disclosure.

A computer program product including a signal-bearing medium bearing oneor more instructions for acquiring subjective user state data includingat least a first subjective user state and a second subjective userstate; one or more instructions for acquiring objective context dataincluding at least a first context data indicative of a first objectiveoccurrence associated with a user and a second context data indicativeof a second objective occurrence associated with the user; and one ormore instructions for correlating the subjective user state data withthe objective context data. In addition to the foregoing, other computerprogram product aspects are described in the claims, drawings, and textforming a part of the present disclosure.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1 a and 1 b show a high-level block diagram of a network deviceoperating in a network environment.

FIG. 2 a shows another perspective of the subjective user state dataacquisition module 102 of the computing device 10 of FIG. 1 b.

FIG. 2 b shows another perspective of the objective context dataacquisition module 104 of the computing device 10 of FIG. 1 b.

FIG. 2 c shows another perspective of the correlation module 106 of thecomputing device 10 of FIG. 1 b.

FIG. 2 d shows another perspective of the presentation module 108 of thecomputing device 10 of FIG. 1 b.

FIG. 2 e shows another perspective of the one or more applications 126of the computing device 10 of FIG. 1 b.

FIG. 3 is a high-level logic flowchart of a process.

FIG. 4 a is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 b is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 c is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 d is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 e is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 5 a is a high-level logic flowchart of a process depictingalternate implementations of the objective context data acquisitionoperation 304 of FIG. 3.

FIG. 5 b is a high-level logic flowchart of a process depictingalternate implementations of the objective context data acquisitionoperation 304 of FIG. 3.

FIG. 5 c is a high-level logic flowchart of a process depictingalternate implementations of the objective context data acquisitionoperation 304 of FIG. 3.

FIG. 5 d is a high-level logic flowchart of a process depictingalternate implementations of the objective context data acquisitionoperation 304 of FIG. 3.

FIG. 5 e is a high-level logic flowchart of a process depictingalternate implementations of the objective context data acquisitionoperation 304 of FIG. 3.

FIG. 6 a is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 306 of FIG. 3.

FIG. 6 b is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 306 of FIG. 3.

FIG. 7 is a high-level logic flowchart of another process.

FIG. 8 a is a high-level logic flowchart of a process depictingalternate implementations of the presentation operation 708 of FIG. 7.

FIG. 8 b is a high-level logic flowchart of a process depictingalternate implementations of the presentation operation 708 of FIG. 7.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here.

A recent trend that is becoming increasingly popular in thecomputing/communication field is to electronically record one'sfeelings, thoughts, and other aspects of the person's everyday life ontoan open diary. One place where such open diaries are maintained are atsocial networking sites commonly known as “blogs” where one or moreusers may report or post the latest news, their thoughts and opinions onvarious topics, and various aspects of the users' everyday life. Theprocess of reporting or posting blog entries is commonly referred to asblogging. Other social networking sites may allow users to update theirpersonal information via social network status reports in which a usermay report or post for others to view the latest status or other aspectsof the user.

A more recent development in social networking is the introduction andexplosive growth of microblogs in which individuals or users (referredto as “microbloggers”) maintain open diaries at microblog websites(e.g., otherwise known as “twitter”) by continuously orsemi-continuously posting microblog entries. A microblog entry (e.g.,“tweet”) is typically a short text message that is usually not more than140 characters long. The microblog entries posted by a microblogger mayreport on any aspect of the microblogger's daily life.

The various things that are typically posted though microblog entriesmay be categorized into one of at least two possible categories. Thefirst category of things that may be reported through microblog entriesare “objective occurrences” associated with the microblogger (e.g.,“user”). Objective occurrences associated with the microblogger (e.g.,user) may be any characteristic, event, happening, or aspect associatedwith or is of interest to the microblogger that can be objectivelyreported by the microblogger, a third party, or by a device. Thesethings would include, for example, food, medicine, or nutraceuticalintake of the microblogger, certain physical characteristics of themicroblogger such as blood sugar level or blood pressure that can beobjectively measured, daily activities of the microblogger observable byothers or by a device, the local weather, the stock market (which themicroblogger may have an interest in), activities of others (e.g.,spouse or boss) that may directly or indirectly affect the microblogger,and so forth.

A second category of things that may be reported or posted throughmicroblogging entries include “subjective states” of the microblogger(e.g., user). Subjective states of a microblogger (e.g., a user) includeany subjective state or status associated with the microblogger that canonly be typically reported by the microblogger (e.g., generally cannotbe reported by a third party or by a device). Such states including, forexample, the mental state of the microblogger (e.g., “I am feelinghappy”), particular physical states of the microblogger (e.g., “my ankleis sore” or “my ankle does not hurt anymore” or “my vision is blurry”),and overall state of the microblogger (e.g., “I'm good” or “I'm well”).Although microblogs are being used to provide a wealth of personalinformation, they have only been primarily limited to their use as ameans for providing commentaries and for maintaining open diaries.

In accordance with various embodiments, methods, systems, and computerprogram products are provided for correlating subjective user state data(e.g., data that indicate subjective user states of a user) withobjective context data (e.g., data that indicate objective occurrencesassociated with the user). In other words, to determine a causalrelationship between objective occurrences (e.g., cause) and subjectiveuser states (e.g., result) associated with a user (e.g., a blogger ormicroblogger). For example, determining that whenever a user eats abanana (e.g., objective occurrence) the user feels “good” (e.g.,subjective user state). Note that an objective occurrence does not needto precede a corresponding subjective user state. For example, a personmay become “gloomy” (e.g., subjective user state) whenever it is aboutto rain (e.g., objective occurrence).

As will be used herein a “subjective user state” is in reference to anystate or status associated with a user (e.g., a blogger or microblogger)that only the user can typically indicate or describe. Such statesinclude, for example, the subjective mental state of the user (e.g.,user is feeling sad), a subjective physical state (e.g., physicalcharacteristic) that only the user can typically indicate (e.g., abackache or an easing of a backache as opposed to blood pressure whichcan be reported by a blood pressure device and/or a third party), or thesubjective overall state of the user (e.g., user is “good”). Examples ofsubjective mental states include, for example, happiness, sadness,depression, anger, frustration, elation, fear, alertness, sleepiness,and so forth. Examples of subjective physical states include, forexample, the presence, easing, or absence of pain, blurry vision,hearing loss, upset stomach, physical exhaustion, and so forth.Subjective overall states may include any subjective user states thatcannot be categorized as a subjective mental state or as a subjectivephysical state. Examples of overall states of a user that may besubjective user states include, for example, user being good, bad,exhausted, lack of rest, user wellness, and so forth.

In contrast, “objective context data” may include data that indicateobjective occurrences associated with the user. An objective occurrencemay be any physical characteristic, event, happenings, or aspectsassociated with or is of interest to a user that can be objectivelyreported by at least a third party or a sensor device. Note, however,that such objective context data does not have to be actually providedby a sensor device or by a third party, but instead, may be reported bythe user himself or herself (e.g., via microblog entries). Examples ofobjectively reported occurrences that could by indicated by theobjective context data include, for example, a user's food, medicine, ornutraceutical intake, the user's location at any given point in time,the user's exercise routine, user's blood pressure, the weather atuser's location, activities associated with third parties, the stockmarket, and so forth.

The term “correlating” as will be used herein is in reference to adetermination of one or more relationships between at least twovariables. In the following exemplary embodiments, the first variable issubjective user state data that represents at least a first and a secondsubjective user state of a user and the second variable is objectivecontext data that represents at least a first and a second objectiveoccurrence associated with the user. Note that each of the at leastfirst and second subjective user states represented by the subjectiveuser state data may represent the same or similar type of subjectiveuser state (e.g., user feels happy) but may be distinct subjective userstates because they occurred at different points in time (e.g., userfeels happy during a point in time and the user being happy again duringanother point in time). Similarly, each of the first and secondobjective occurrences represented by the objective context data mayrepresent the same or similar type of objective occurrence (e.g., usereating a banana) but may be distinct objective occurrences because theyoccurred at different points in time (e.g., user ate a banana during apoint in time and the user eating another banana during another point intime).

Various techniques may be employed for correlating the subjective userstate data with the objective context data. For example, in someembodiments, correlating the objective context data with the subjectiveuser state data may be accomplished by determining time sequentialpatterns or relationships between reported objective occurrencesassociated with a user and reported subjective user states of the user.

The following illustrative example is provided to describe howsubjective user states and objective occurrences associated with a usermay be correlated according to some embodiments. Suppose, for example, auser such as a microblogger reports that the user ate a banana on aMonday. The consumption of the banana, in this example, is a reportedfirst objective occurrence associated with the user. The user thenreports that 15 minutes after eating the banana, the user felt veryhappy. The reporting of the emotional state (e.g., felt very happy) is,in this example, a reported first subjective user state. On Tuesday, theuser reports that the user ate another banana (e.g., a second objectiveoccurrence associated with the user). The user then reports that 15minutes after eating the second banana, the user felt somewhat happy(e.g., a second subjective user state). For purposes of this example,the reporting of the consumption of the bananas may be in the form ofobjective context data and the reporting of the user feeling very orsomewhat happy may be in the form of subjective user state data. Thereported information may then be examined from different perspectives inorder to determine whether there is a correlation (e.g., relationship)between the subjective user state data indicating the subjective userstates (e.g., happiness of the user) and the objective context dataindicating the objective occurrences associated with the user (e.g.,eating bananas).

Several approaches may be employed in various alternativeimplementations in order to determine whether there is correlation(e.g., a relationship) between the subjective user state data and theobjective context data. For example, a determination may be made as towhether there is co-occurrence, temporal sequencing, temporal proximity,and so forth, between the subjective user states (e.g., as provided bythe subjective user state data) and the objective occurrences (e.g., asprovided by the objective context data) associated with the user. One ormore factors may be relevant in the determination of whether there iscorrelation between the subjective user state data and the objectivecontext data.

One factor that may be examined in order to determine whether arelationship exists between the subjective user state data (e.g.,happiness of the user) and the objective context data (e.g., consumptionof bananas) is whether the first and second objective occurrences (e.g.,consuming a banana) of the user are the same or similar (e.g., extent ofsimilarity or difference). In this case, the first and second objectiveoccurrences are the same. Note that consumption of the bananas couldhave been further defined. For example, the quantity or the type ofbananas consumed could have been specified. If the quantity or the typeof bananas consumed were not the same, then this could negatively impactthe correlation (e.g., determination of a relationship) of thesubjective user state data (e.g., happiness of the user) with theobjective context data (e.g., eating bananas).

Another relevant factor that could be examined is whether the first andsecond subjective user states of the user are the same or similar (e.g.,extent of similarity or difference). In this case, the first subjectiveuser state (e.g., felt very happy) and second subjective user states(e.g., felt somewhat happy) are not the same but are similar. In thiscase, the comparison of the two subjective user states indicates thatthe two subjective user states, although not the same, are similar. Thismay result ultimately in a determination of a weaker correlation betweenthe subjective user state data and the objective context data.

A third relevant factor that may be examined is whether the timedifference between the first subjective user state and the firstobjective occurrence associated with the user (e.g., 15 minutes) and thetime difference between the second subjective user state and the secondobjective occurrence associated with the user (e.g., 15 minutes) are thesame or similar. In this case, the time difference between the firstsubjective user state and the first objective occurrence associated withthe user (e.g., 15 minutes) and the time difference between the secondsubjective user state and the second objective occurrence associatedwith the user (e.g., 15 minutes) are indeed the same. As a result, thismay indicate a relatively strong correlation between the subjective userstate data (e.g., happiness of the user) and the objective context data(e.g., eating of bananas by the user). This operation is a relativelysimple way of determining time sequential patterns. Note that if thetime difference between the first subjective user state and the firstobjective occurrence associated with the user and the time differencebetween the second subjective user state and the second objectiveoccurrence associated with the user (e.g., 15 minutes) were not the sameor not similar, a weaker correlation or no correlation between thesubjective user state data (e.g., happiness of the user) and theobjective context data (e.g., eating of bananas by the user) may beconcluded. Further, if the time differences were large (e.g., there wasa four hour gap between the reporting of a consumption of a banana andthe feeling of happiness), then this may indicate a weaker correlationbetween the subjective user state data (e.g., happiness of the user) andthe objective context data (e.g., eating of bananas by the user).

The review of the subjective user state data and the objective contextdata from these perspectives may facilitate in determining whether thereis a correlation between such data. That is, by examining such data fromthe various perspectives as described above, a determination may be madeas to whether there is a sequential relationship between subjective userstates (e.g., happiness of the user) and objective occurrences (e.g.,consumption of bananas) associated with the user. Of course, thoseskilled in the art will recognize that the correlation of subjectiveuser state data with objective context data may be made with greaterconfidence if more data points are obtained. For instance, in the aboveexample, a stronger relationship may be determined between thesubjective user state data (e.g., happiness of the user) and theobjective context data (e.g., consumption of bananas) if additional datapoints with respect to the subjective user state data (e.g., a thirdsubjective user state, a fourth subjective user state, and so forth) andthe objective context data (e.g., a third objective occurrence, a fourthobjective occurrence, and so forth) were obtained and analyzed.

In alternative embodiments, other techniques may be employed in order tocorrelate subjective user state data with objective context data. Forexample, one approach is to determine whether a subjective user staterepeatedly occurres before, after, or at least partially concurrentlywith an objective occurrence. For instance, a determination may be madeas to whether a user repeatedly has a stomach ache (e.g., subjectiveuser state) each time after eating a banana (e.g., objectiveoccurrence). In another example, a determination may be made as towhether a user repeatedly feels gloomy (e.g., subjective user state)before each time it begins to rain (e.g., objective occurrence). Instill another example, a determination may be made as to whether a userrepeatedly feels happy (e.g., subjective user state) each time his bossleaves town (e.g., objective occurrence).

FIGS. 1 a and 1 b illustrate an example environment in accordance withvarious embodiments. In the illustrated environment, an exemplary system100 may include at least a computing device 10 (see FIG. 1 b) that maybe employed in order to, among other things, collect subjective userstate data 60 and objective context data 70* that are associated with auser 20*, and to correlate the subjective user state data 60 with theobjective context data 70*. Note that in the following, “*” indicates awildcard. Thus, user 20* may indicate a user 20 a or a user 20 b ofFIGS. 1 a and 1 b.

In some embodiments, the computing device 10 may be a network server inwhich case the computing device 10 may communicate with a user 20 a viaa mobile device 30 and through a wireless and/or wired network 40. Notethat a network server as described herein may be in reference to anetwork server located at a single network site or located acrossmultiple network sites or a conglomeration of servers located atmultiple network sites. The mobile device 30 may be a variety ofcomputing/communication devices including, for example, a cellularphone, a personal digital assistant (PDA), a laptop, or some other typeof mobile computing/communication device. In alternative embodiments,the computing device 10 may be a local computing device thatcommunicates directly with a user 20 b. For these embodiments, thecomputing device 10 may be any type of handheld device such as acellular telephone or a PDA, or other types of computing/communicationdevices such as a laptop computer, a desktop computer, and so forth. Incertain embodiments, the computing device 10 may be a peer-to-peernetwork component device. In some embodiments, the local device 30 mayoperate via web 2.0 construct.

In embodiments where the computing device 10 is a server, the computingdevice 10 may indirectly obtain the subjective user state data 60 from auser 20 a via the mobile device 30. In alternative embodiments in whichthe computing device 10 is a local device, the subjective user statedata 60 may be directly obtained from a user 20 b. As will be furtherdescribed, the computing device 10 may acquire the objective contextdata 70* from one or more different sources.

For ease of illustration and explanation, the following systems andoperations to be described herein will be generally described in thecontext of the computing device 10 being a network server. However,those skilled in the art will recognize that these systems andoperations may also be implemented when the computing device 10 is alocal device communicating directly with a user 20 b.

Assuming that the computing device 10 is a server, the computing device10 may be configured to acquire subjective user state data 60 includingdata indicating at least a first subjective user state 60 a and a secondsubjective user state 60 b via the mobile device 30 and through wirelessand/or wired networks 40. In some embodiments, the data indicating thefirst subjective user state 60 a and the second subjective user state 60b may be in the form of blog entries, such as microblog entries, orembodied in some other form of electronic messages. The first subjectiveuser state 60 a and the second subjective user state 60 b may, in someinstances, indicate the same, similar, or completely differentsubjective user state. Examples of subjective user states indicated bythe first subjective user state 60 a and the second subjective userstate 60 b include, for example, a subjective mental state of the user20 a (e.g., user 20 a is sad or angry), a subjective physical state ofthe user 20 a (e.g., physical or physiological characteristic of theuser 20 a such as the presence or absence of a stomach ache orheadache), a subjective overall state of the user 20 a (e.g., user is“well”), or other subjective user states that only the user 20 a cantypically indicate.

The computing device 10 may be further configured to acquire objectivecontext data 70* from one or more sources. For instance, objectivecontext data 70 a may be acquired, in some instances, from one or morethird parties 50 (e.g., other users, a health care provider, a hospital,a place of employment, a content provider, and so forth). In somealternative situations, objective context data 70 b may be acquired fromone or more sensors 35 (e.g., blood pressure device or glucometer)sensing, for example, one or more physical characteristics of the user20 a. Note that the one or more sensors 35 may be other types of sensorsfor measuring and providing to the computing device 10 other subjectiveoccurrences associated with user 20 a. For example, in some cases,sensors 35 may include a global positioning system (GPS) device fordetermining the location of the user 20 a or a physical activity sensorfor measuring physical activities of the user 20 a. Examples of aphysical activity sensor include, for example, a pedometer for measuringphysical activities of the user 20 a. In some implementations, the oneor more sensors 35 may include one or more physiological sensor devicesfor measuring physiological characteristics of the user 20 a. Examplesof physiological sensor devices include, for example, a blood pressuremonitor, a heart rate monitor, a glucometer, and so forth. In someimplementations, the one or more sensors 35 may include one or moreimage capturing devices such as a video or digital camera.

In still other situations, objective context data 70 c may be acquiredfrom the user 20 a via the mobile device 30. For these situations, theobjective context data 70 c may indicate, for example, activities (e.g.,exercise or food or medicine intake) performed by the user 20 a, certainphysical characteristics (e.g., blood pressure or location) associatedwith the user 20 a, or other aspects associated with the user 20 a thatthe user 20 a can report objectively. In still other alternative cases,objective context data 70 d may be acquired from a memory 140.

In various embodiments, the context data 70* acquired by the computingdevice 10 may include at least a first context data indicative of afirst objective occurrence associated with the user 20 a and a secondcontext data indicative of a second objective occurrence associated withthe user 20 a. In some implementations, the first and second contextdata may be acquired in the form of blog entries (e.g., microblogentries) or in other forms of electronic messages.

The computing device 10 may be further configured to correlate theacquired subjective user data 60 with the acquired context data 70*. Bycorrelating the acquired subjective user data 60 with the acquiredcontext data 70*, a determination may be made as to whether there is arelationship between the acquired subjective user data 60 with theacquired context data 70*. In some embodiments, and as will be furtherindicated in the operations and processes to be described herein, thecomputing device 10 may be further configured to present one or more theresults of correlation. In various embodiments, the one or morecorrelation results 80 may be presented to the user 20 a and/or to oneor more third parties 50. The one or more third parties 50 may be otherusers such as other microbloggers, a health care provider, advertisers,and/or content providers.

As illustrated in FIG. 1 b, computing device 10 may include one or morecomponents or sub-modules. For instance, in various implementations,computing device 10 may include a subjective user state data acquisitionmodule 102, an objective context data acquisition module 104, acorrelation module 106, a presentation module 108, a network interface120, a user interface 122, a time stamp module 124, one or moreapplications 126, and/or memory 140. The functional roles of thesecomponents/modules will be described in the processes and operations tobe described herein.

FIG. 2 a illustrates particular implementations of the subjective userstate data acquisition module 102 of the computing device 10 of FIG. 1b. In brief, the subjective user state data acquisition module 102 maybe designed to, among other things, acquire subjective user state data60 including at least a first subjective user state 60 a and a secondsubjective user state 60 b. As further illustrated, the subjective userstate data acquisition module 102 in various implementations may includea reception module 202 for receiving the subjective user state data 60from a user 20 a via the network interface 120 or for receiving thesubjective user state data 60 directly from a user 20 b (e.g., in thecase where the computing device 10 is a local device) via the userinterface 122.

In some implementations, the reception module 202 may further include atext entry reception module 204 for receiving subjective user state datathat was obtained based, at least in part, on a text entry provided by auser 20*. For example, in some implementations the text entry receptionmodule 204 may be designed to receive subjective user state data 60 thatwas obtained based, at least in part, on a text entry (e.g., a textmicroblog entry) provided by a user 20 a using a mobile device 30. In analternative implementation or the same implementation, the receptionmodule 202 may include an audio entry reception module 205 for receivingsubjective user state data that was obtained based, at least in part, onan audio entry provided by a user 20*. For example, in someimplementations the audio entry reception module 205 may be designed toreceive subjective user state data 60 that was obtained based, at leastin part, on an audio entry (e.g., an audio microblog entry) provided bya user 20 a using a mobile device 30.

In some implementations, the subjective user state data acquisitionmodule 102 may include a solicitation module 206 for soliciting from auser 20* a subjective user state. For example, the solicitation module206, in some implementations, may be designed to solicit from a user 20b, via a user interface 122 (e.g., in the case where the computingdevice 10 is a local device), a subjective user state of the user 20 b(e.g., whether the user 20 b is feeling very good, good, bad, or verybad). The solicitation module 206 may further include a transmissionmodule 207 for transmitting to a user 20 a a request requesting asubjective user state 60*. For example, the transmission module 207 maybe designed to transmit to a user 20 a, via a network interface 122, arequest requesting a subjective user state 60*. The solicitation module206 may be used in some circumstances in order to prompt the user 20* toprovide useful data. For instance, if the user 20* has reported a firstsubjective user state 60 a following a first objective occurrence, thenthe solicitation module 206 may solicit from the user 20* a secondsubjective user state 60 b following the happening of the secondobjective occurrence.

Referring now to FIG. 2 b illustrating particular implementations of theobjective context data acquisition module 104 of the computing device 10of FIG. 1 b. In various implementations, the objective context dataacquisition module 104 may be configured to acquire (e.g., eitherreceive, solicit, or retrieve from a user 20*, a third party 50, asensor 35, and/or a memory 140) objective context data 70* including atleast a first context data indicative of a first objective occurrenceassociated with a user 20* and a second context data indicative of asecond objective occurrence associated with the user 20*. In someimplementations, the objective context data acquisition module 104 mayinclude an objective context data reception module 208 that isconfigured to receive objective context data 70*. For example, theobjective context data reception module 208 may be designed to receive,via a user interface 122 or a network interface 120, context data from auser 20*, from a third party 50, and/or from a sensor 35.

Turning now to FIG. 2 c illustrating particular implementations of thecorrelation module 106 of the computing device 10 of FIG. 1 b. Thecorrelation module 106 may be configured to, among other things,correlate subjective user state data 60 with objective context data 70*.In some implementations, the correlation module 106 may include asubjective user state difference determination module 210 fordetermining an extent of difference between a first subjective userstate 60 a and a second subjective user state 60 b associated with auser 20*. In the same or different implementations, the correlationmodule 106 may include a objective occurrence difference determinationmodule 212 for determining an extent of difference between at least afirst objective occurrence and a second objective occurrence associatedwith a user 20*.

In the same or different implementations, the correlation module 106 mayinclude a subjective user state and objective occurrence time differencedetermination module 214. As will be further described below, thesubjective user state and objective occurrence time differencedetermination module 214 may be configured to determine at least anextent of time difference between a subjective user state associatedwith a user 20* and an objective occurrence associated with the user20*. In the same or different implementations, the correlation module106 may include a comparison module 216 for comparing an extent of timedifference between a first subjective user state and a first objectiveoccurrence associated with a user 20* with the extent of time differencebetween a second subjective user state and a second objective occurrenceassociated with the user 20*.

In the same or different implementations, the correlation module 106 mayinclude a strength of correlation determination module 218 fordetermining a strength of correlation between subjective user state dataand objective context data associated with a user 20*. In someimplementations, the strength of correlation may be determined based, atleast in part, on results provided by the objective occurrencedifference determination module 210, the objective occurrence differencedetermination module 212, the subjective user state and objectiveoccurrence time difference determination module 214 and/or thecomparison module 216. In some implementations, and as will be furtherdescribed herein, the correlation module 106 may include a determinationmodule 219 for determining whether a subjective user state occurredbefore, after, or at least partially concurrently with an objectiveoccurrence associated with a user 20*.

FIG. 2 d illustrates particular implementations of the presentationmodule 108 of the computing device 10 of FIG. 1 b. In variousimplementations, the presentation module 108 may be configured topresent one or more results of the correlation performed by thecorrelation module 106. For example, in some implementations this mayentail the presentation module 108 presenting to the user 20* anindication of a sequential relationship between a subjective user stateand an objective occurrence associated with the user 20* (e.g.,“whenever you eat a banana, you have a stomachache). Other types ofresults may also be presented in other alternative implementations aswill be further described herein.

In various implementations, the presentation module 108 may include atransmission module 220 for transmitting one or more results of thecorrelation performed by the correlation module 106. For example, in thecase where the computing device 10 is a server, the transmission module220 may be configured to transmit to the user 20 a or a third party 50the one or more results of the correlation performed by the correlationmodule 106 via a network interface 120.

In some alternative implementations, the presentation module may includea display module 222 for displaying the one or more results of thecorrelation performed by the correlation module 106. For example, in thecase where the computing device 10 is a local device, the display module222 may be configured to display to the user 20 b the one or moreresults of the correlation performed by the correlation module 106 via auser interface 122.

Referring back to FIG. 1 b, and as briefly described earlier, in someimplementations, the computing device 10 may include a time stamp module124. For these implementations, the time stamp module 124 may beconfigured to provide time stamps for objective occurrences and/orsubjective user states associated with a user 20*. For example, if thecomputing device 10 is a local device that communicates directly with auser 20 a, then the time stamp module 124 may generate a first timestamp for the first subjective user state 60 a and a second time stampfor the second subjective user state 60 b. Note that the time stampsprovided by the time stamp module 124 may be associated with subjectiveuser states and/or objective occurrences rather than being associatedwith subjective user state data 60 and/or objective context data. 70*.That is, the times in which the subjective user states and/or theobjective occurrences occurred may be more relevant than when theseevents were actually reported (e.g., reported via microblog entries).

In various embodiments, the computing device 10 may include a networkinterface 120 that may facilitate in communicating with a user 20 aand/or one or more third parties 50. For example, in embodiments wherebythe computing device 10 is a server, the computing device 10 may includea network interface 120 that may be configured to receive from the user20 a subjective user state data 60. In some embodiments, objectivecontext data 70 a, 70 b, or 70 c may be received through thecommunication interface 120. Examples of a network interface 120includes, for example, a network interface card (NIC).

In various embodiments, the computing device 10 may include a userinterface 122 to communicate directly with a user 20 b. For example, inembodiments in which the computing device 10 is a local device, the userinterface 122 may be configured to directly receive from the user 20 bsubjective user state data 60. The user interface 122 may include, forexample, one or more of a display monitor, a touch screen, a key board,a mouse, an audio system, and/or other user interface devices.

FIG. 2 e illustrates particular implementations of the one or moreapplications 126 of FIG. 1 b. For these implementations, the one or moreapplications 126 may include, for example, communication applicationssuch as a text messaging application and/or an audio messagingapplication including a voice recognition system application. In someimplementations, the one or more applications 126 may include a web 2.0application 230 to facilitate communication via, for example, the WorldWide Web.

FIG. 3 illustrates an operational flow 300 representing exampleoperations related to acquisition and correlation of subjective userstate data and objective context data in accordance with variousembodiments. In some embodiments, the operational flow 300 may beexecuted by, for example, the computing device 10 of FIG. 1 b.

In FIG. 3 and in the following figures that include various examples ofoperational flows, discussions and explanations may be provided withrespect to the above-described exemplary environment of FIGS. 1 a and 1b, and/or with respect to other examples (e.g., as provided in FIGS. 2a-2 e) and contexts. However, it should be understood that theoperational flows may be executed in a number of other environments andcontexts, and/or in modified versions of FIGS. 1 a, 1 b, and 2 a-2 e.Also, although the various operational flows are presented in thesequence(s) illustrated, it should be understood that the variousoperations may be performed in other orders than those which areillustrated, or may be performed concurrently.

Further, in FIG. 3 and in following figures, various operations may bedepicted in a box-within-a-box manner. Such depictions may indicate thatan operation in an internal box may comprise an optional exampleembodiment of the operational step illustrated in one or more externalboxes. However, it should be understood that internal box operations maybe viewed as independent operations separate from any associatedexternal boxes and may be performed in any sequence with respect to allother illustrated operations, or may be performed concurrently.

In any event, after a start operation, the operational flow 300 may moveto a subjective user state data acquisition operation 302 for acquiringsubjective user state data including at least a first subjective userstate and a second subjective user state as performed by, for example,the computing device 10 of FIG. 1 b. For instance, the subjective userstate data acquisition module 102 of the computing device 10 acquiringsubjective user state data 60 (e.g., in the form of text or audiomicroblog entries) including at least a first subjective user state 60 a(e.g., the user 20* is feeling sad) and a second subjective user state60 b (e.g., the user 20* is again feeling sad).

Operational flow 300 further includes an objective context dataacquisition operation 304 for acquiring objective context data includingat least a first context data indicative of a first objective occurrenceassociated with a user and a second context data indicative of a secondobjective occurrence associated with the user as performed by, forexample, the computing device 10. For instance, the objective contextdata acquisition module 104 of the computing device 10 acquiring via awireless and/or wired network 40 objective context data 70* (e.g., asprovided by a third party source or by the user 20 a) including at leasta first context data 70* indicative of a first occurrence (e.g., cloudyweather) associated with a user 20* and a second context data 70*indicative of a second occurrence (e.g., cloudy weather) associated withthe user 20*. Note that, and as those skilled in the art will recognize,the subjective user state data acquisition operation 302 does not haveto be performed prior to the objective context data acquisitionoperation 304 and may be performed subsequent to the performance of theobjective context data acquisition operation 304 or may be performedconcurrently with the objective context data acquisition operation 304.

Finally, a correlation operation 306 for correlating the subjective userstate data with the objective context data may be performed by, forexample, the computing device 10. For instance, the correlation module106 of the computing device 10 correlating the subjective user statedata 60 with the objective context data 70* by determining a sequentialtime relationship between the subjective user state data 60 and theobjective context data 70* (e.g., user 20* will be sad whenever it iscloudy).

In various implementations, the subjective user state data acquisitionoperation 302 may include one or more additional operations asillustrated in FIGS. 4 a, 4 b, 4 c, and 4 d. For example, in someimplementations the subjective user state data acquisition operation 302may include a reception operation 402 for receiving at least a firstsubjective user state as depicted in FIG. 4 a to 4 c. For instance, thereception module 202 (see FIG. 2 a) of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) atleast a first subjective user state 60 a (e.g., indicating a firstsubjective mental, physical, or overall state of a user 20*).

In various alternative implementations, the reception operation 402 mayfurther include one or more additional operations. For example, in someimplementations, reception operation 402 may include an operation 404for receiving a first subjective user state from at least one of awireless network or a wired network as depicted in FIG. 4 a. Forinstance, the reception module 202 (see FIG. 2 a) of the computingdevice 10 receiving (e.g., receiving via the network interface 120) afirst subjective user state 60 a (e.g., a first subjective overall stateof the user 20 a indicating, for example, user wellness) from at leastone of a wireless network or a wired network 40.

In various implementations, the reception operation 402 may include anoperation 406 for receiving a first subjective user state via anelectronic message generated by the user as illustrated in FIG. 4 a. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via a network interface 120) a first subjective user state 60 a(e.g., a first subjective mental state of the user 20 a indicating, forexample, user anger) via an electronic message (e.g., text or audiomessage) generated by the user 20 a.

In some implementations, the reception operation 402 may include anoperation 408 for receiving a first subjective user state via a firstblog entry generated by the user as depicted in FIG. 4 a. For instance,the reception module 202 of the computing device 10 receiving (e.g., viaa network interface 120) a first subjective user state 60 a (e.g., afirst subjective physical state of the user 20 a indicating, forexample, the presence or absence of pain) via a first blog entrygenerated by the user 20 a.

In some implementations, the reception operation 402 may include anoperation 409 for receiving a first subjective user state via a statusreport generated by the user as depicted in FIG. 4 a. For instance, thereception module 202 of the computing device 10 receiving (e.g., througha network interface 120) a first subjective user state via a statusreport (e.g., a social network status report, a collaborativeenvironment status report, a shared browser status report, or some otherstatus report) generated by the user 20 a.

In various implementations, the reception operation 402 may include anoperation 410 for receiving a second subjective user state via anelectronic message generated by the user as depicted in FIG. 4 a. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via a network interface 120) a second subjective user state 60 b(e.g., a second subjective mental state of the user 20 a indicating, forexample, user anger) via an electronic message (e.g., text or audiomessage) generated by the user 20 a.

In some implementations, the reception operation 402 may further includean operation 412 for receiving a second subjective user state via asecond blog entry generated by the user as depicted in FIG. 4 a. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via a network interface 120) a second subjective user state(e.g., a second subjective physical state of the user 20 a indicating,for example, the presence or absence of pain) via a second blog entrygenerated by the user 20 a.

In some implementations, the reception operation 402 may further includean operation 413 for receiving a second subjective user state via astatus report generated by the user as depicted in FIG. 4 a. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via a network interface 120) a second subjective user state via astatus report (e.g., a social network status report, a collaborativeenvironment status report, a shared browser status report, or some otherstatus report) generated by the user 20 a.

In various implementations, the reception operation 402 may include anoperation 414 for receiving a first subjective user state that wasobtained based, at least in part, on data provided by the user, theprovided data indicating the first subjective user state associated withthe user as depicted in FIG. 4 a. For instance, the reception module 202of the computing device 10 receiving (e.g., via the network interface120 or via the user interface 122) a first subjective user state (e.g.,a first subjective mental, physical, or overall state of the user 20*)that was obtained based, at least in part, on data provided by the user20*, the provided data indicating the first subjective user stateassociated with the user 20*.

In some implementations, operation 414 may further include an operation416 for receiving a first subjective user state that was obtained based,at least in part, on a text entry provided by the user as depicted inFIG. 4 a. For instance, the text entry reception module 204 (see FIG. 2a) of the computing device 10 receiving (e.g., via the network interface120 or the user interface 122) a first subjective user state 60 a (e.g.,a subjective mental, physical, or overall state of the user 20*) thatwas obtained based, at least in part, on a text entry provided by theuser 20*.

In some implementations, operation 414 may further include an operation418 for receiving a first subjective user state that was obtained based,at least in part, on an audio entry provided by the user as depicted inFIG. 4 a. For instance, the audio entry reception module 206 (see FIG. 2a) of the computing device 10 receiving (e.g., via the network interface120 or the user interface 122) a first subjective user state 60 a (e.g.,a subjective mental, physical, or overall state of the user 20*) thatwas obtained based, at least in part, on an audio entry provided by theuser 20*.

In some implementations, operation 414 may further include an operation419 for receiving a first subjective user state that was obtained based,at least in part, on an image entry provided by the user as depicted inFIG. 4 a. For instance, the reception module 202 of the computing device10 receiving (e.g., via the network interface 120 or via the userinterface 122) a first subjective user state 60 a that was obtainedbased, at least in part, on an image entry (e.g., to capture a gesturesuch a “thumbs up” gesture or to capture a facial expression such as agrimace made by the user 20*) provided by the user 20*.

In various implementations, the reception operation 402 may include anoperation 420 for receiving a first subjective user state indicating asubjective mental state of the user as depicted in FIG. 4 b. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) afirst subjective user state 60 a indicating a subjective mental state(e.g., feeling happy or drowsy) of the user 20*.

In some implementations, operation 420 may further include an operation422 for receiving a first subjective user state indicating a level ofthe subjective mental state of the user as depicted in FIG. 4 a. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) afirst subjective user state 60 a indicating a level of the subjectivemental state (e.g., feeling extremely happy or very drowsy) of the user20*.

The reception operation 402 in various implementations may include anoperation 424 for receiving a first subjective user state indicating asubjective physical state of the user as depicted in FIG. 4 b. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) afirst subjective user state 60 a (e.g., as provided by user 20* via atext or audio entry) indicating a subjective physical state (e.g.,absence or presence of a headache or sore back) of the user 20*.

In some implementations, operation 424 may further include an operation426 for receiving a first subjective user state indicating a level ofthe subjective physical state of the user as depicted in FIG. 4 b. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) afirst subjective user state 60 a indicating a level of the subjectivephysical state (e.g., absence or presence of a very bad headache or avery sore back) of the user 20*.

In various implementations, the reception operation 402 may include anoperation 428 for receiving a first subjective user state indicating asubjective overall state of the user as depicted in FIG. 4 b. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) afirst subjective user state 60 a indicating a subjective overall state(e.g., user 20* is “well”) of the user 20*.

In some implementations, operation 428 may further include an operation430 for receiving a first subjective user state indicating a level ofthe subjective overall state of the user as depicted in FIG. 4 b. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) afirst subjective user state 60 a indicating a level of the subjectiveoverall state (e.g., user is “very well”) of the user 20*.

In certain implementations, the reception operation 402 may include anoperation 432 for receiving a second subjective user state that wasobtained based, at least in part, on data provided by the user, theprovided data indicating the second subjective user state associatedwith the user as depicted in FIG. 4 b. For instance, the receptionmodule 202 of the computing device 10 receiving (e.g., via the networkinterface 120 or via the user interface 122) a second subjective userstate 60 b (e.g., a second subjective mental, physical, or overall stateof the user 20*) that was obtained based, at least in part, on dataprovided by the user 20*, the provided data indicating the secondsubjective user state associated with the user 20*.

In some implementations, operation 432 may further include an operation434 for receiving a second subjective user state that was obtainedbased, at least in part, on a text entry provided by the user asdepicted in FIG. 4 b. For instance, the text entry reception module 204(see FIG. 2 a) of the computing device 10 receiving (e.g., via thenetwork interface 120 or the user interface 122) a second subjectiveuser state 60 b (e.g., a subjective mental, physical, or overall stateof the user 20*) that was obtained based, at least in part, on a textentry provided by the user 20*.

In some implementations, operation 432 may further include an operation436 for receiving a second subjective user state that was obtainedbased, at least in part, on an audio entry provided by the user asdepicted in FIG. 4 b. For instance, the audio entry reception module 206(see FIG. 2 a) of the computing device 10 receiving (e.g., via thenetwork interface 120 or the user interface 122) a second subjectiveuser state 60 b (e.g., a subjective mental, physical, or overall stateof the user 20*) that was obtained based, at least in part, on an audioentry provided by the user 20*.

In some implementations, operation 432 may further include an operation437 for receiving a second subjective user state that was obtainedbased, at least in part, on an image entry provided by the user Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) asecond subjective user state 60 b that was obtained based, at least inpart, on an image entry (e.g., to capture a gesture such a “thumbs down”gesture or to capture a facial expression such as a smile made by theuser 20*) provided by the user 20*.

In various implementations, the reception operation 402 may include anoperation 438 for receiving a second subjective user state indicating asubjective mental state of the user as depicted in FIG. 4 b. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) asecond subjective user state 60 b indicating a subjective mental state(e.g., feeling sad or alert) of the user 20*.

In some implementations, operation 438 may further include an operation440 for receiving a second subjective user state indicating a level ofthe subjective mental state of the user as depicted in FIG. 4 b. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) asecond subjective user state 60 b indicating a level of the subjectivemental state (e.g., feeling extremely sad or extremely alert) of theuser 20*.

The reception operation 402, in various implementations, may include anoperation 442 for receiving a second subjective user state indicating asubjective physical state of the user as depicted in FIG. 4 c. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) asecond subjective user state 60 b indicating a subjective physical state(e.g., having blurry vision or being nauseous) of the user 20*.

In some implementations, operation 442 may further include an operation444 for receiving a second subjective user state indicating a level ofthe subjective physical state of the user as depicted in FIG. 4 c. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) asecond subjective user state 60 b indicating a level of the subjectivephysical state (e.g., having slightly blurry vision or being slightlynauseous) of the user 20*.

In various implementations, the reception operation 402 may include anoperation 446 for receiving a second subjective user state indicating asubjective overall state of the user as depicted in FIG. 4 c. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) asecond subjective user state 60 b indicating a subjective overall state(e.g., user 20* is “exhausted”) of the user 20*.

In some implementations, operation 446 may further include an operation448 for receiving a second subjective user state indicating a level ofthe subjective overall state of the user as depicted in FIG. 4 c. Forinstance, the reception module 202 of the computing device 10 receiving(e.g., via the network interface 120 or via the user interface 122) asecond subjective user state 60 b indicating a level of the subjectiveoverall state (e.g., user 20* is “extremely exhausted”) of the user 20*.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 450 for acquiring a first timestamp associated with the first subjective user state and a second timestamp associated with the second subjective user state as depicted inFIG. 4 c. For instance, the subjective user state data acquisitionmodule 102 of the computing device 10 acquiring (e.g., receiving via thenetwork interface 120 or generating via time stamp module 124) a firsttime stamp associated with the first subjective user state 60 a and asecond time stamp associated with the second subjective user state 60 b.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 452 for acquiring subjective userstate data including at least a first subjective user state and a secondsubjective user state that is equivalent to the first subjective userstate as depicted in FIG. 4 d. For instance, the subjective user statedata acquisition module 102 acquiring (e.g., via network interface 120or via user interface 122) subjective user state data 60 including atleast a first subjective user state (e.g., user 20* feels sleepy) and asecond subjective user state (e.g., user 20* feels sleepy) that isequivalent to the first subjective user state 60 a.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 454 for acquiring subjective userstate data including at least a first subjective user state and a secondsubjective user state that is proximately equivalent to the firstsubjective user state as depicted in FIG. 4 d. For instance, thesubjective user state data acquisition module 102 acquiring (e.g., vianetwork interface 120 or via user interface 122) subjective user statedata 60 including at least a first subjective user state 60 a (e.g.,user 20* feels angry) and a second subjective user state 60 b (e.g.,user 20* feels extremely angry) that is proximately equivalent to thefirst subjective user state 60 a.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 455 for soliciting from the userat least one of the first subjective user state or the second subjectiveuser state as depicted in FIG. 4 d. For instance, the solicitationmodule 206 (see FIG. 2 a) of the computing device 10 soliciting from theuser 20* (e.g., via network interface 120 or via user interface 122) atleast one of the first subjective user state 60 a (e.g., mental,physical, or overall user state) or the second subjective user state 60b (e.g., mental, physical, or overall user state).

In some implementations, operation 455 may further include an operation456 for transmitting to the user a request for a subjective user stateas depicted in FIG. 4 d. For instance, the transmission module 207 (seeFIG. 2 a) of the computing device 10 transmitting (e.g., via the networkinterface 120) to the user 20 a a request for a subjective user state.In some cases, the request may provide to the user 20 a an option tomake a section from a number of alternatives subjective user states(e.g., are you happy, very happy, sad, or very sad?).

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 457 for acquiring at least one ofthe first subjective user state or the second subjective user state at aserver as depicted in FIG. 4 d. For instance, the subjective user statedata acquisition module 102 of the computing device 10 acquiring atleast one of the first subjective user state 60 a (e.g., user is“sleepy”) or the second subjective user state 60 b (e.g., user is again“sleepy”) at a server (e.g., computing device 10 being a networkserver).

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 458 for acquiring at least one ofthe first subjective user state or the second subjective user state at ahandheld device as depicted in FIG. 4 d. For instance, the subjectiveuser state data acquisition module 102 of the computing device 10acquiring at least one of the first subjective user state 60 a (e.g.,user is “dizzy”) or the second subjective user state 60 b (e.g., user isagain “dizzy”) at a handheld device (e.g., computing device 10 being amobile phone or a PDA).

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 460 for acquiring at least one ofthe first subjective user state or the second subjective user state at apeer-to-peer network component device as depicted in FIG. 4 d. Forinstance, the subjective user state data acquisition module 102 of thecomputing device 10 acquiring at least one of the first subjective userstate 60 a (e.g., user feels “alert”) or the second subjective userstate 60 b (e.g., user again feels “alert”) at a peer-to-peer networkcomponent device (e.g., computing device 10).

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 462 for acquiring at least one ofthe first subjective user state or the second subjective user via a Web2.0 construct as depicted in FIG. 4 d. For instance, the subjective userstate data acquisition module 102 of the computing device 10 acquiringat least one of the first subjective user state 60 a (e.g., user feelsill) or the second subjective user 60 b (e.g., user again feels ill) viaa Web 2.0 construct.

In some implementations, the subjective user state data acquisitionoperation 302 may include an operation 464 for acquiring data thatindicates a first subjective user state that occurred at least partiallyconcurrently with an occurrence of a first objective occurrenceassociated with the user as depicted in FIG. 4 e. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring (e.g., via network interface 120 or via userinterface 122) data that indicates a first subjective user state thatoccurred at least partially concurrently with an occurrence of a firstobjective occurrence associated with the user 20*.

In some implementations, the subjective user state data acquisitionoperation 302 may include an operation 466 for acquiring data thatindicates a second subjective user state that occurred at leastpartially concurrently with an occurrence of a second objectiveoccurrence associated with the user as depicted in FIG. 4 e. Forinstance, the subjective user state data acquisition module 102 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) data that indicates a second subjective user statethat occurred at least partially concurrently with an occurrence of asecond objective occurrence associated with the user 20*.

In some implementations, the subjective user state data acquisitionoperation 302 may include an operation 468 for acquiring data thatindicates a first subjective user state that occurred prior to anoccurrence of a first objective occurrence associated with the user asdepicted in FIG. 4 e. For instance, the subjective user state dataacquisition module 102 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) data that indicates afirst subjective user state that occurred prior to an occurrence of afirst objective occurrence associated with the user 20* (e.g., firstsubjective user state occurred within a predefined time increment beforethe occurrence of the first objective occurrence such as occurringwithin 15 minutes, 30 minutes, 1 hour, 1 day, or some other timeincrement before the occurrence of the first objective occurrence).

In some implementations, the subjective user state data acquisitionoperation 302 may include an operation 470 for acquiring data thatindicates a second subjective user state that occurred prior to anoccurrence of a second objective occurrence associated with the user asdepicted in FIG. 4 e. For instance, the subjective user state dataacquisition module 102 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) data that indicates asecond subjective user state that occurred prior to an occurrence of asecond objective occurrence associated with the user 20* (e.g., secondsubjective user state occurred within a predefined time increment beforethe occurrence of the second objective occurrence such as occurringwithin 15 minutes, 30 minutes, 1 hour, 1 day, or some other predefinedtime increment before the occurrence of the second objectiveoccurrence).

In some implementations, the subjective user state data acquisitionoperation 302 may include an operation 472 for acquiring data thatindicates a first subjective user state that occurred subsequent to anoccurrence of a first objective occurrence associated with the user asdepicted in FIG. 4 e. For instance, the subjective user state dataacquisition module 102 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) data that indicates afirst subjective user state that occurred subsequent to an occurrence ofa first objective occurrence associated with the user 20* (e.g., firstsubjective user state occurred within a predefined time increment afterthe occurrence of the first objective occurrence such as occurringwithin 15 minutes, 30 minutes, 1 hour, 1 day, or some other predefinedtime increment after the occurrence of the first objective occurrence).

In some implementations, the subjective user state data acquisitionoperation 302 may include an operation 474 for acquiring data thatindicates a second subjective user state that occurred subsequent to anoccurrence of a second objective occurrence associated with the user asdepicted in FIG. 4 e. For instance, the subjective user state dataacquisition module 102 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) data that indicates asecond subjective user state that occurred subsequent to an occurrenceof a second objective occurrence associated with the user 20* (e.g.,second subjective user state occurred within a predefined time incrementafter the occurrence of the second objective occurrence such asoccurring within 15 minutes, 30 minutes, 1 hour, 1 day, or some othertime increment after the occurrence of the second objective occurrence).

Referring back to FIG. 3, in various implementations the objectivecontext data acquisition operation 304 may include one or moreadditional operations as illustrated in FIGS. 5 a, 5 b, 5 c, 5 d, and 5e. For example, in some implementations, the objective context dataacquisition operation 304 may include a reception operation 502 forreceiving the objective context data as depicted in FIG. 5 a. Forinstance, the objective context data reception module 208 of thecomputing device 10 receiving (e.g., via a network interface 120 or viaa user interface 122) the objective context data 70 a, 70 b, or 70 c.

In some implementations, the reception operation 502 may further includeone or more additional operations. For example, in some implementations,the reception operation 502 may include an operation 504 for receivingthe objective context data from at least one of a wireless network orwired network as depicted in FIG. 5 a. For instance, the objectivecontext data reception module 208 of the computing device 10 receiving(e.g., via network interface 120) the objective context data 70 a, 70 b,or 70 c from at least one of a wireless network or wired network 40.

In some implementations, the reception operation 502 may include anoperation 506 for receiving the objective context data via one or moreblog entries as depicted in FIG. 5 a. For instance, the objectivecontext data reception module 208 of the computing device 10 receiving(e.g., via network interface 120) the objective context data 70 a or 70c via one or more blog entries (e.g., microblog entries).

In some implementations, the reception operation 502 may include anoperation 507 for receiving the objective context data via one or morestatus reports as depicted in FIG. 5 a. For instance, the objectivecontext data reception module 208 of the computing device 10 receiving(e.g., via network interface 120) the objective context data 70 a or 70c via one or more status reports (e.g., social network status reports).

In some implementations, the reception operation 502 may include anoperation 508 for receiving the objective context data via a Web 2.0construct as depicted in FIG. 5 a. For instance, the objective contextdata reception module 208 of the computing device 10 receiving (e.g.,via network interface 120) the objective context data 70 a, 70 b, or 70c via a Web 2.0 construct (e.g., web 2.0 application 230).

In various implementations, the reception operation 502 may include anoperation 510 for receiving the objective context data from one or morethird party sources as depicted in FIG. 5 b. For instance, the objectivecontext data reception module 208 of the computing device 10 receiving(e.g., via network interface 120) the objective context data 70 a fromone or more third party sources 50.

In some implementations, operation 510 may further include an operation512 for receiving the objective context data from at least one of ahealth care professional, a pharmacy, a hospital, a health careorganization, a health monitoring service, or a health care clinic asdepicted in FIG. 5 b. For instance, the objective context data receptionmodule 208 of the computing device 10 receiving (e.g., via networkinterface 120) the objective context data 70 a from at least one of ahealth care professional, a pharmacy, a hospital, a health careorganization, a health monitoring service, or a health care clinic.

In some implementations, operation 510 may further include an operation514 for receiving the objective context data from a content provider asdepicted in FIG. 5 b. For instance, the objective context data receptionmodule 208 of the computing device 10 receiving (e.g., via networkinterface 120) the objective context data 70 a from a content provider.

In some implementations, operation 510 may further include an operation516 for receiving the objective context data from at least one of aschool, a place of employment, or a social group as depicted in FIG. 5b. For instance, the objective context data reception module 208 of thecomputing device 10 receiving (e.g., via network interface 120) theobjective context data 70 a from at least one of a school, a place ofemployment, or a social group.

In various implementations, the reception operation 502 may include anoperation 518 for receiving the objective context data from one or moresensors configured to sense one or more objective occurrences associatedwith the user as depicted in FIG. 5 c. For instance, the objectivecontext data reception module 208 of the computing device 10 receiving(e.g., via network interface 120) the objective context data 70 b fromone or more sensors 35 configured to sense one or more objectiveoccurrences (e.g., blood pressure, blood sugar level, location of theuser 20 a, and so forth) associated with the user 20 a.

In some implementations, operation 518 may further include an operation520 for receiving the objective context data from a physical activitysensor device as depicted in FIG. 5 c. For instance, the objectivecontext data reception module 208 of the computing device 10 receiving(e.g., via network interface 120) the objective context data 70 b from aphysical activity sensor device (e.g., a pedometer or a sensor on anexercise machine).

In some implementations, operation 518 may further include an operation521 for receiving the objective context data from a global positioningsystem (GPS) device as depicted in FIG. 5 c. For instance, the objectivecontext data reception module 208 of the computing device 10 receiving(e.g., via network interface 120) the objective context data 70 b from aglobal positioning system (GPS) device (e.g., mobile device 30).

In some implementations, operation 518 may further include an operation522 for receiving the objective context data from a physiological sensordevice as depicted in FIG. 5 c. For instance, the objective context datareception module 208 of the computing device 10 receiving (e.g., vianetwork interface 120) the objective context data 70 b from aphysiological sensor device (e.g., blood pressure monitor, heart ratemonitor, glucometer, and so forth).

In some implementations, operation 518 may further include an operation523 for receiving the objective context data from an image capturingdevice as depicted in FIG. 5 c. For instance, the objective context datareception module 208 of the computing device 10 receiving (e.g., vianetwork interface 120) the objective context data 70 b from an imagecapturing device (e.g., video or digital camera).

In various implementations, the reception operation 502 may include anoperation 524 for receiving the objective context data from the user asdepicted in FIG. 5 c. For instance, the objective context data receptionmodule 208 of the computing device 10 receiving (e.g., via networkinterface 120 or via user interface 122) the objective context data 70 cfrom the user 20*.

In various implementations, the objective context data acquisitionoperation 304 of FIG. 3 may include an operation 525 for acquiring theobjective context data from a memory as depicted in FIG. 5 c. Forinstance, the objective context data acquisition module 104 of thecomputing device 10 acquiring the objective context data 70 d (e.g.,tidal chart or moon phase chart) from memory 140.

In various implementations, the objective context data acquisitionoperation 304 may include an operation 528 for acquiring at least afirst context data indicative of a first objective occurrence associatedwith a user and a second context data indicative of a second objectiveoccurrence associated with the user that is equivalent to the firstobjective occurrence as depicted in FIG. 5 c. For instance, theobjective context data acquisition module 104 of the computing device 10acquiring at least a first context data indicative of a first objectiveoccurrence (e.g., cloudy weather) associated with a user 20* and asecond context data indicative of a second objective occurrence (e.g.,cloudy weather) associated with the user 20* that is equivalent to thefirst objective occurrence.

In various implementations, the objective context data acquisitionoperation 304 may include an operation 530 for acquiring at least afirst context data indicative of a first objective occurrence associatedwith a user and a second context data indicative of a second objectiveoccurrence associated with the user that is proximately equivalent tothe first objective occurrence as depicted in FIG. 5 c. For instance,the objective context data acquisition module 104 of the computingdevice 10 acquiring at least a first context data indicative of a firstobjective occurrence (e.g., drank 8 cans of beer) associated with a user20* and a second context data indicative of a second objectiveoccurrence (e.g., drank 7 cans of beer) associated with the user 20*that is proximately equivalent to the first objective occurrence.

In various implementations, the objective context data acquisitionoperation 304 may include an operation 532 for acquiring a first timestamp associated with the first objective occurrence and a second timestamp associated with the second objective occurrence as depicted inFIG. 5 d. For instance, the objective context data acquisition module104 of the computing device 10 acquiring (e.g., receiving via networkinterface 120 or generating via time stamp module 124) a first timestamp associated with the first objective occurrence (e.g., jogged for40 minutes) and a second time stamp associated with the second objectiveoccurrence (e.g., jogged for 38 minutes).

In various implementations, the objective context data acquisitionoperation 304 may include an operation 534 for acquiring a first contextdata indicative of a first activity performed by the user and a secondcontext data indicative of a second activity performed by the user asdepicted in FIG. 5 d. For instance, the objective context dataacquisition module 104 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) a first context dataindicative of a first activity (e.g., ingesting a particular food,medicine, or nutraceutical) performed by the user and a second contextdata indicative of a second activity (e.g., ingesting the same orsimilar particular food, medicine, or nutraceutical) performed by theuser 20*.

In some implementations, operation 534 may also include an operation 536for acquiring a first context data indicative of an ingestion by theuser of a first medicine and a second context data indicative of aningestion by the user of a second medicine as depicted in FIG. 5 d. Forinstance, the objective context data acquisition module 104 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) a first context data indicative of an ingestion bythe user 20* of a first medicine (e.g., 600 mg dose of ibuprofen) and asecond context data indicative of an ingestion by the user of a secondmedicine e.g., another 600 mg dose of ibuprofen).

In some implementations, operation 534 may also include an operation 538for acquiring a first context data indicative of an ingestion by theuser of a first food and a second context data indicative of aningestion by the user of a second food as depicted in FIG. 5 d. Forinstance, the objective context data acquisition module 104 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) a first context data indicative of an ingestion bythe user 20* of a first food (e.g., 16 ounces of orange juice) and asecond context data indicative of an ingestion by the user 20* of asecond food (e.g., another 16 ounces of orange juice).

In some implementations, operation 534 may also include an operation 540for acquiring a first context data indicative of an ingestion by theuser of a first nutraceutical and a second context data indicative of aningestion by the user of a second nutraceutical as depicted in FIG. 5 d.For instance, the objective context data acquisition module 104 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) a first context data indicative of an ingestion bythe user 20* of a first nutraceutical (e.g., a serving of ginkgo biloba)and a second context data indicative of an ingestion by the user 20* ofa second nutraceutical (e.g., a serving of ginkgo biloba).

In some implementations, operation 534 may also include an operation 542for acquiring a first context data indicative of a first exerciseroutine executed by the user and a second context data indicative of asecond exercise routine executed by the user as depicted in FIG. 5 d.For instance, the objective context data acquisition module 104 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) a first context data indicative of a first exerciseroutine (e.g., exercising 30 minutes on a treadmill machine) executed bythe user 20* and a second context data indicative of a second exerciseroutine (e.g., exercising another 30 minutes on the treadmill machine)executed by the user 20*.

In some implementations, operation 534 may also include an operation 544for acquiring a first context data indicative of a first social activityexecuted by the user and a second context data indicative of a secondsocial activity executed by the user as depicted in FIG. 5 d. Forinstance, the objective context data acquisition module 104 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) a first context data indicative of a first socialactivity (e.g., going out on a blind date) executed by the user 20* anda second context data indicative of a second social activity (e.g.,going out again on a blind date) executed by the user 20*.

In some implementations, operation 534 may also include an operation 546for acquiring a first context data indicative of a first work activityexecuted by the user and a second context data indicative of a secondwork activity executed by the user as depicted in FIG. 5 d. Forinstance, the objective context data acquisition module 104 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) a first context data indicative of a first workactivity (e.g., two hours of overtime work) executed by the user 20* anda second context data indicative of a second work activity (e.g.,another two hours of overtime work) executed by the user 20*.

In various implementations, the objective context data acquisitionoperation 304 of FIG. 3 may include an operation 548 for acquiring afirst context data indicative of a first activity performed by a thirdparty and a second context data indicative of a second activityperformed by the third party as depicted in FIG. 5 e. For instance, theobjective context data acquisition module 104 of the computing device 10acquiring (e.g., via network interface 120 or via user interface 122) afirst context data indicative of a first activity performed by a thirdparty (e.g., dental procedure performed by a dentist on the user 20* asreported by the dentist or by the user 20*) and a second context dataindicative of a second activity performed by the third party (e.g.,another dental procedure performed by a dentist on the user 20* asreported by the dentist or by the user 20*).

In some implementations, operation 548 may further include an operation550 for acquiring a first context data indicative of a first socialactivity executed by the third party and a second context dataindicative of a second social activity executed by the third party asdepicted in FIG. 5 e. For instance, the objective context dataacquisition module 104 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) a first context dataindicative of a first social activity executed by the third party (e.g.,spouse going away to visit a relative) and a second context dataindicative of a second social activity executed by the third party(e.g., spouse going away again to visit a relative).

In some implementations, operation 548 may further include an operation552 for acquiring a first context data indicative of a first workactivity executed by the third party and a second context dataindicative of a second work activity executed by the third party asdepicted in FIG. 5 e. For instance, the objective context dataacquisition module 104 of the computing device 10 acquiring (e.g., vianetwork interface 120 or via user interface 122) a first context dataindicative of a first work activity executed by the third party (e.g.,boss meeting with the user 20*) and a second context data indicative ofa second work activity executed by the third party (e.g., boss meetingwith the user 20*).

In various implementations, the objective context data acquisitionoperation 304 of FIG. 3 may include an operation 554 for acquiring afirst context data indicative of a first physical characteristic of theuser and a second context data indicative of a second physicalcharacteristic of the user as depicted in FIG. 5 e. For instance, theobjective context data acquisition module 104 of the computing device 10acquiring (e.g., via network interface 120 or via user interface 122) afirst context data indicative of a first physical characteristic of theuser 20* (e.g., high blood sugar level) and a second context dataindicative of a second physical characteristic of the user 20* (e.g.,another high blood sugar level).

In various implementations, the objective context data acquisitionoperation 304 may include an operation 556 for acquiring a first contextdata indicative of a first external event and a second context dataindicative of a second external event as depicted in FIG. 5 e. Forinstance, the objective context data acquisition module 104 of thecomputing device 10 acquiring (e.g., via network interface 120 or viauser interface 122) a first context data indicative of a first externalevent (e.g., stock market drops 500 points) and a second context dataindicative of a second external event (e.g., stock market again drops500 points).

In various implementations, the objective context data acquisitionoperation 304 may include an operation 558 for acquiring a first contextdata indicative of a first location of the user and a second contextdata indicative of a second location of the user as depicted in FIG. 5e. For instance, the objective context data acquisition module 104 ofthe computing device 10 acquiring (e.g., via network interface 120 orvia user interface 122) a first context data indicative of a firstlocation (e.g., Hawaii) of the user 20* (e.g., during a first point intime) and a second context data indicative of a second location (e.g.,Hawaii) of the user 20* (e.g., during second point in time).

In various implementations, the objective context data acquisitionoperation 304 may include an operation 560 for acquiring a first timestamp associated with the first objective occurrence and a second timestamp associated with the second objective occurrence as depicted inFIG. 5 e. For instance, the objective context data acquisition module104 of the computing device 10 acquiring (e.g., via network interface120 or via time stamp module 124) a first time stamp associated with thefirst objective occurrence (e.g., consumption of medicine) and a secondtime stamp associated with the second objective occurrence (e.g.,consumption again of the same or similar medicine).

Referring back to FIG. 3, the correlation operation 306 may include oneor more additional operations as illustrated in FIGS. 6 a and 6 b. Forexample, in various implementations, the correlation operation 306 mayinclude an operation 602 for determining at least an extent of timedifference between the first subjective user state associated with theuser and the first objective occurrence associated with the user asdepicted in FIG. 6 a. For instance, the subjective user state andobjective occurrence time difference determination module 214 (see FIG.2 c) of the computing device 10 determining at least an extent of timedifference between the occurrence of the first subjective user state(e.g., an extreme hangover) associated with the user 20* and theoccurrence of the first objective occurrence (e.g., drinking four shotsof whiskey) associated with the user 20* by, for example, comparing atime stamp associated with the first subjective user state with a timestamp associated with the first objective occurrence.

In some implementations, operation 602 may further include an operation604 for determining at least an extent of time difference between thesecond subjective user state associated with the user and the secondobjective occurrence associated with the user as depicted in FIG. 6 a.For instance, the subjective user state and objective occurrence timedifference determination module 214 of the computing device 10determining at least an extent of time difference between the secondsubjective user state (e.g., a slight hangover) associated with the user20* and the second objective occurrence (e.g., again drinking two shotsof whiskey) associated with the user 20* by, for example, comparing atime stamp associated with the second subjective user state with a timestamp associated with the second objective occurrence.

In some implementations, operation 604 may further include an operation606 for comparing the extent of time difference between the firstsubjective user state and the first objective occurrence with the extentof time difference between the second subjective user state and thesecond objective occurrence as depicted in FIG. 6 a. For instance, thecomparison module 216 (see FIG. 2 c) of the computing device 10comparing the extent of time difference between the first subjectiveuser state (e.g., an extreme hangover) and the first objectiveoccurrence (e.g., drinking four shots of whiskey) with the extent oftime difference between the second subjective user state (e.g., a slighthangover) and the second objective occurrence (e.g., drinking two shotsof whiskey).

In various implementations, the correlation operation 306 may include anoperation 608 for determining an extent of difference between the firstsubjective user state and the second subjective user state associatedwith the user as depicted in FIG. 6 a. For instance, the subjective userstate difference determination module 210 (see FIG. 2 c) of thecomputing device 10 determining an extent of difference between thefirst subjective user state (e.g., an extreme hangover) and the secondsubjective user state (e.g., a slight hangover) associated with the user20*. Such an operation may be implemented to, for example, determinewhether there is a relationship between a subjective user state (e.g., alevel of hangover) and an objective occurrence (e.g., amount ofconsumption of whiskey) or in determining a strength of correlationbetween the subjective user state and the objective occurrence.

In various implementations, the correlation operation 306 may include anoperation 610 for determining an extent of difference between the firstobjective occurrence and the second objective occurrence associated withthe user as depicted in FIG. 6 a. For instance, the objective occurrencedifference determination module 212 (see FIG. 2 c) determining an extentof difference between the first objective occurrence (e.g., drinkingfour shots of whiskey) and the second objective occurrence (e.g.,drinking two shots of whiskey) associated with the user 20*. Such anoperation may be implemented to, for example, determine whether there isa relationship between a subjective user state (e.g., a level ofhangover) and an objective occurrence (e.g., amount of consumption ofwhiskey) or in determining a strength of correlation between thesubjective user state and the objective occurrence.

In various implementations, the correlation operation 306 may include anoperation 612 for determining a strength of the correlation between thesubjective user state data and the objective context data as depicted inFIG. 6 a. For instance, the strength of correlation determination module218 (see FIG. 2 c) of the computing device 10 determining a strength ofthe correlation between the subjective user state data (e.g., hangover)and the objective context data (e.g., drinking whiskey).

In some implementations, the correlation operation 306 may include anoperation 614 for determining whether the first subjective user stateoccurred after occurrence of the first objective occurrence associatedwith the user as depicted in FIG. 6 b. For instance, the determinationmodule 219 of the computing device 10 determining whether the firstsubjective user state (e.g., upset stomach) occurred after occurrence ofthe first objective occurrence (e.g., eating a banana) associated withthe user 20* (e.g., determining whether the first subjective user stateoccurred within a predefined time increment after the occurrence of thefirst objective occurrence such as determining whether the firstsubjective user state occurring within 15 minutes, 30 minutes, 1 hour, 1day, or some other time increment after the occurrence of the firstobjective occurrence).

In some implementations, the correlation operation 306 may include anoperation 616 for determining whether the second subjective user stateoccurred after occurrence of the second objective occurrence associatedwith the user as depicted in FIG. 6 b. For instance, the determinationmodule 219 of the computing device 10 determining whether the secondsubjective user state (e.g., upset stomach) occurred after occurrence ofthe second objective occurrence (e.g., eating a banana) associated withthe user 20* (e.g., determining whether the second subjective user stateoccurred within a predefined time increment after the occurrence of thesecond objective occurrence such as determining whether the firstsubjective user state occurring within 15 minutes, 30 minutes, 1 hour, 1day, or some other time increment after the occurrence of the secondobjective occurrence).

In some implementations, the correlation operation 306 may include anoperation 618 for determining whether the first subjective user stateoccurred before occurrence of the first objective occurrence associatedwith the user as depicted in FIG. 6 b. For instance, the determinationmodule 219 of the computing device 10 determining whether the firstsubjective user state (e.g., feeling gloomy) occurred before occurrenceof the first objective occurrence (e.g., raining weather) associatedwith the user 20* (e.g., determining whether the first subjective userstate occurred within a predefined time increment before the occurrenceof the first objective occurrence such as determining whether the firstsubjective user state occurring within 15 minutes, 30 minutes, 1 hour, 1day, or some other time increment before the occurrence of the firstobjective occurrence).

In some implementations, the correlation operation 306 may include anoperation 620 for determining whether the second subjective user stateoccurred before occurrence of the second objective occurrence associatedwith the user as depicted in FIG. 6 b. For instance, the determinationmodule 219 of the computing device 10 determining whether the secondsubjective user state (e.g., feeling gloomy) occurred before occurrenceof the second objective occurrence (e.g., raining weather) associatedwith the user 20* (e.g., determining whether the second subjective userstate occurred within a predefined time increment before the occurrenceof the second objective occurrence such as determining whether thesecond subjective user state occurring within 15 minutes, 30 minutes, 1hour, 1 day, or some other time increment before the occurrence of thesecond objective occurrence).

In some implementations, the correlation operation 306 may include anoperation 622 for determining whether the first subjective user stateoccurred at least partially concurrently with occurrence of the firstobjective occurrence associated with the user as depicted in FIG. 6 b.For instance, the determination module 219 of the computing device 10determining whether the first subjective user state (e.g., happiness)occurred at least partially concurrently with occurrence of the firstobjective occurrence (e.g., boss left town) associated with the user20*.

In some implementations, the correlation operation 306 may include anoperation 624 for determining whether the second subjective user stateoccurred at least partially concurrently with occurrence of the secondobjective occurrence associated with the user as depicted in FIG. 6 b.For instance, the determination module 219 of the computing device 10determining whether the second subjective user state (e.g., happiness)occurred at least partially concurrently with occurrence of the secondobjective occurrence (e.g., boss left town) associated with the user20*.

FIG. 7 illustrates another operational flow 700 related to acquisitionand correlation of subjective user state data and objective contextdata, and for presenting one or more results of the correlation inaccordance with various embodiments. The operational flow 700 mayinclude at least a subjective user state data acquisition operation 702,an objective context data acquisition operation 704, and a correlationoperation 706 that corresponds to and mirror the subjective user statedata acquisition operation 302, the objective context data acquisitionoperation 304, and the correlation operation 306, respectively, of theoperational flow 300 of FIG. 3. In addition, operational flow 700includes a presentation operation 708 for presenting one or more resultsof the correlating of the subjective user state data and the objectivecontext data. For instance, the presentation module 108 of the computingdevice 10 presenting (e.g., displaying via the user interface 122 ortransmitting via the network interface 120) one or more results of thecorrelating of the subjective user state data 60 with the objectivecontext data 70*.

The presentation operation 702 may include one or more additionaloperations in various alternative implementations as illustrated inFIGS. 8 a and 8 b. For example, in some implementations, thepresentation operation 702 may include a transmission operation 801 fortransmitting the one or more results as depicted in FIG. 8 a. Forinstance, the transmission module 220 (see FIG. 2 d) of the computingdevice 10 transmitting (e.g., via the network interface 120) the one ormore results of the correlation of the subjective user state data withthe objective context data.

In some implementations, the transmission operation 801 may include anoperation 802 for transmitting the one or more results to the user asdepicted in FIG. 8 a. For instance, the transmission module 220 of thecomputing device 10 transmitting (e.g., via the network interface 120)the one or more results of the correlating of the subjective user statedata 60 with the objective context data 70* to the user 20 a.

In some implementations, the transmission operation 801 may include anoperation 804 for transmitting the one or more results to one or morethird parties as depicted in FIG. 8 a. For instance, the transmissionmodule 220 of the computing device 10 transmitting (e.g., via thenetwork interface 120) the one or more results of the correlating of thesubjective user state data 60 with the objective context data 70* to oneor more third parties 50.

In some implementations, the presentation operation 708 may include anoperation 806 for displaying the one or more results to the user via auser interface as depicted in FIG. 8 a. For instance, the display module222 (see FIG. 2 d) of the computing device 10 displaying the one or moreresults of the correlating of the subjective user state data 60 with theobjective context data 70* to the user 20* via a user interface 122(e.g., display monitor and/or an audio device). Note that as used herein“displaying” may refer to the showing of the one or more resultsthrough, for example, a display monitor, and/or audibly indicating theone or more results via an audio device.

In some implementations, the presentation operation 708 may include anoperation 808 for presenting an indication of a sequential relationshipbetween a subjective user state and an objective occurrence associatedwith the user as depicted in FIG. 8 a. For instance, the presentationmodule 108 of the computing device 10 presenting (e.g., via a networkinterface 120 or a user interface 122) an indication of a sequentialrelationship between a subjective user state (e.g., hangover) and anobjective occurrence (e.g., consuming at least two shots of whiskey)associated with the user 20*. In this example, the presented indicationmay indicate that the user 20* will have a headache after drinking twoor more shots of whiskey.

In some implementations, the presentation operation 708 may include anoperation 810 for presenting a prediction of a future subjective userstate resulting from a future occurrence associated with the user asdepicted in FIG. 8 a. For instance, the presentation module 108 of thecomputing device 10 presenting (e.g., via a network interface 120 or auser interface 122) a prediction of a future subjective user state(e.g., sadness) resulting from a future occurrence (e.g., missing son'sfootball game) associated with the user 20*. In this example, thepresented indication may indicate that the user 20* will be sad if theuser misses his son's football game.

In some implementations, the presentation operation 708 may include anoperation 811 for presenting a prediction of a future subjective userstate resulting from a past occurrence associated with the user asdepicted in FIG. 8 a. For instance, the presentation module 108 of thecomputing device 10 presenting (e.g., via a network interface 120 or auser interface 122) a prediction of a future subjective user state(e.g., you will get a stomach ache) resulting from a past occurrence(e.g., ate a banana) associated with the user 20*.

In some implementations, the presentation operation 708 may include anoperation 812 for presenting a past subjective user state associatedwith a past occurrence associated with the user as depicted in FIG. 8 a.For instance, the presentation module 108 of the computing device 10presenting (e.g., via a network interface 120 or a user interface 122) apast subjective user state associated with a past occurrence associatedwith the user 20* (e.g., “did you know that whenever the user drinksgreen tea, the user always feels alert?”).

In some implementations, the presentation operation 708 may include anoperation 814 for presenting a recommendation for a future action asdepicted in FIG. 8 a. For instance, the presentation module 108 of thecomputing device 10 presenting (e.g., via a network interface 120 or auser interface 122) a recommendation for a future action (e.g., “youshould take a dose of brand x aspirin for your headaches”). Note that inthis example, the consumption of the brand x aspirin is the objectiveoccurrence and the stopping or easing of a headache is the subjectiveuser state.

In particular implementations, operation 814 may further include anoperation 816 for presenting a justification for the recommendation asdepicted in FIG. 8 a. For instance, the presentation module 108 of thecomputing device 10 presenting (e.g., via a network interface 120 or auser interface 122) a justification for the recommendation (e.g., “brandx aspirin in the past seems to work the best for your headaches”).

In some implementations, the presentation operation 708 may include anoperation 818 for presenting an indication of a strength of correlationbetween the subjective user state data and the objective context data asdepicted in FIG. 8 b. For instance, the presentation module 108 of thecomputing device 10 presenting (e.g., via a network interface 120 or auser interface 122) an indication of a strength of correlation betweenthe subjective user state data 60 and the objective context data 70*(e.g., “you sometimes get a headache after a night of drinkingwhiskey”).

In various implementations, the presentation operation 708 may includean operation 820 for presenting one or more results of the correlatingin response to a reporting of an occurrence of a third objectiveoccurrence associated with the user as depicted in FIG. 8 b. Forinstance, the presentation module 108 of the computing device 10presenting (e.g., via a network interface 120 or a user interface 122)one or more results of the correlating (e.g., going to Hawaii causesuser's allergies to act up) in response to a reporting (e.g., via amicroblog entry or by other means) of an occurrence of a third objectiveoccurrence (e.g., leaving for Hawaii) associated with the user 20*.

In various implementations, operation 820 may include one or moreadditional operations. For example, in some implementations, operation820 may include an operation 822 for presenting one or more results ofthe correlating in response to a reporting of an event that was executedby the user as depicted in FIG. 8 b. For instance, the presentationmodule 108 of the computing device 10 presenting (e.g., via a networkinterface 120 or a user interface 122) one or more results of thecorrelating (e.g., drinking two or more shots of whiskey causes ahangover) in response to a reporting of an event (e.g., reporting a shotof whiskey being drunk) that was executed by the user 20*,

In some implementations, operation 820 may include an operation 824 forpresenting one or more results of the correlating in response to areporting of an event that was executed by a third party as depicted inFIG. 8 b. For instance, the presentation module 108 of the computingdevice 10 presenting (e.g., via a network interface 120 or a userinterface 122) one or more results (e.g., indication that the usershould not drive) of the correlating (e.g., vision is always blurryafter being sedated by a dentist) in response to a reporting of an event(e.g., sedation of the user by the dentist) that was executed by a thirdparty 50 (e.g., dentist).

In some implementations, operation 820 may include an operation 826 forpresenting one or more results of the correlating in response to areporting of an occurrence of an external event as depicted in FIG. 8 b.For instance, the presentation module 108 of the computing device 10presenting (e.g., via a network interface 120 or a user interface 122)one or more results of the correlating (e.g., indication that the useris always depressed after the stock market drops more than 500 points)in response to a reporting of an occurrence of an external event (e.g.,stock market drops 700 points).

In various implementations, the presentation operation 708 may includean operation 828 for presenting one or more results of the correlatingin response to a reporting of an occurrence of a third subjective userstate as depicted in FIG. 8 b. For instance, the presentation module 108of the computing device 10 presenting (e.g., via a network interface 120or a user interface 122) one or more results of the correlating (e.g.,taking brand x aspirin stops headaches) in response to a reporting of anoccurrence of a third subjective user state (e.g., user has a headache).

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those having skill in the art will recognize that it is common withinthe art to describe devices and/or processes in the fashion set forthherein, and thereafter use engineering practices to integrate suchdescribed devices and/or processes into data processing systems. Thatis, at least a portion of the devices and/or processes described hereincan be integrated into a data processing system via a reasonable amountof experimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.Furthermore, it is to be understood that the invention is defined by theappended claims.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should typically be interpreted to mean at least the recitednumber (e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.).

In those instances where a convention analogous to “at least one of A,B, or C, etc.” is used, in general such a construction is intended inthe sense one having skill in the art would understand the convention(e.g., “a system having at least one of A, B, or C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

1. A computationally-implemented method, comprising: acquiringsubjective user state data including at least a first subjective userstate and a second subjective user state; acquiring objective contextdata including at least a first context data indicative of a firstobjective occurrence associated with a user and a second context dataindicative of a second objective occurrence associated with the user;and correlating the subjective user state data with the objectivecontext data, wherein said correlating the subjective user state datawith the objective context data is performed via at least one of amachine, article of manufacture, or composition of matter.
 2. Thecomputationally-implemented method of claim 1, wherein said acquiringsubjective user state data including at least a first subjective userstate and a second subjective user state comprises: receiving at least afirst subjective user state.
 3. The computationally-implemented methodof claim 2, wherein said receiving at least a first subjective userstate comprises: receiving a first subjective user state via a firstblog entry generated by the user.
 4. The computationally-implementedmethod of claim 2, wherein said receiving at least a first subjectiveuser state comprises: receiving a first subjective user state via astatus report generated by the user.
 5. The computationally-implementedmethod of claim 2, wherein said receiving at least a first subjectiveuser state comprises: receiving a second subjective user state via asecond blog entry generated by the user.
 6. Thecomputationally-implemented method of claim 2, wherein said receiving atleast a first subjective user state comprises: receiving a secondsubjective user state via a status report generated by the user.
 7. Thecomputationally-implemented method of claim 2, wherein said receiving atleast a first subjective user state comprises: receiving a firstsubjective user state that was obtained based, at least in part, on dataprovided by the user, the provided data indicating the first subjectiveuser state associated with the user.
 8. The computationally-implementedmethod system of claim 2, wherein said receiving at least a firstsubjective user state comprises: receiving a first subjective user stateindicating a subjective mental state of the user.
 9. Thecomputationally-implemented method system of claim 2, wherein saidreceiving at least a first subjective user state comprises: receiving afirst subjective user state indicating a subjective physical state ofthe user.
 10. The computationally-implemented method system of claim 2,wherein said receiving at least a first subjective user state comprises:receiving a first subjective user state indicating a subjective overallstate of the user.
 11. The computationally-implemented method of claim2, wherein said receiving at least a first subjective user statecomprises: receiving a second subjective user state that was obtainedbased, at least in part, on data provided by the user, the provided dataindicating the second subjective user state associated with the user.12. The computationally-implemented method of claim 2, wherein saidreceiving at least a first subjective user state comprises: receiving asecond subjective user state indicating a subjective mental state of theuser.
 13. The computationally-implemented method of claim 2, whereinsaid receiving at least a first subjective user state comprises:receiving a second subjective user state indicating a subjectivephysical state of the user.
 14. The computationally-implemented methodof claim 2, wherein said receiving at least a first subjective userstate comprises: receiving a second subjective user state indicating asubjective overall state of the user.
 15. Thecomputationally-implemented method of claim 1, wherein said acquiringsubjective user state data including at least a first subjective userstate and a second subjective user state comprises: acquiring a firsttime stamp associated with the first subjective user state and a secondtime stamp associated with the second subjective user state.
 16. Thecomputationally-implemented method of claim 1, wherein said acquiringobjective context data including at least a first context dataindicative of a first objective occurrence associated with a user and asecond context data indicative of a second objective occurrenceassociated with the user comprises: receiving the objective contextdata.
 17. The computationally-implemented method of claim 16, whereinsaid receiving the objective context data comprises: receiving theobjective context data via one or more blog entries.
 18. Thecomputationally-implemented method of claim 16, wherein said receivingthe objective context data comprises: receiving the objective contextdata via one or more status reports.
 19. The computationally-implementedmethod of claim 16, wherein said receiving the objective context datacomprises: receiving the objective context data from one or more thirdparty sources.
 20. The computationally-implemented method of claim 16,wherein said receiving the objective context data comprises: receivingthe objective context data from one or more sensors configured to senseone or more objective occurrences associated with the user.
 21. Thecomputationally-implemented method of claim 16, wherein said receivingthe objective context data comprises: receiving the objective contextdata from the user.
 22. The computationally-implemented method of claim1, wherein said acquiring objective context data including at least afirst context data indicative of a first objective occurrence associatedwith a user and a second context data indicative of a second objectiveoccurrence associated with the user comprises: acquiring a first contextdata indicative of a first activity performed by the user and a secondcontext data indicative of a second activity performed by the user. 23.The computationally-implemented method of claim 1, wherein saidacquiring objective context data including at least a first context dataindicative of a first objective occurrence associated with a user and asecond context data indicative of a second objective occurrenceassociated with the user comprises: acquiring a first context dataindicative of a first activity performed by a third party and a secondcontext data indicative of a second activity performed by the thirdparty.
 24. The computationally-implemented method of claim 1, whereinsaid acquiring objective context data including at least a first contextdata indicative of a first objective occurrence associated with a userand a second context data indicative of a second objective occurrenceassociated with the user comprises: acquiring a first context dataindicative of a first physical characteristic of the user and a secondcontext data indicative of a second physical characteristic of the user.25. The computationally-implemented method of claim 1, wherein saidacquiring objective context data including at least a first context dataindicative of a first objective occurrence associated with a user and asecond context data indicative of a second objective occurrenceassociated with the user comprises: acquiring a first context dataindicative of a first external event and a second context dataindicative of a second external event.
 26. Thecomputationally-implemented method of claim 1, wherein said acquiringobjective context data including at least a first context dataindicative of a first objective occurrence associated with a user and asecond context data indicative of a second objective occurrenceassociated with the user comprises: acquiring a first time stampassociated with the first objective occurrence and a second time stampassociated with the second objective occurrence.
 27. Thecomputationally-implemented method of claim 1, wherein said correlatingthe subjective user state data with the objective context datacomprises: determining whether the first subjective user state occurredafter occurrence of the first objective occurrence associated with theuser.
 28. The computationally-implemented method of claim 1, whereinsaid correlating the subjective user state data with the objectivecontext data comprises: determining whether the second subjective userstate occurred after occurrence of the second objective occurrenceassociated with the user.
 29. The computationally-implemented method ofclaim 1, wherein said correlating the subjective user state data withthe objective context data comprises: determining whether the firstsubjective user state occurred before occurrence of the first objectiveoccurrence associated with the user.
 30. The computationally-implementedmethod of claim 1, wherein said correlating the subjective user statedata with the objective context data comprises: determining whether thesecond subjective user state occurred before occurrence of the secondobjective occurrence associated with the user.
 31. Thecomputationally-implemented method of claim 1, wherein said correlatingthe subjective user state data with the objective context datacomprises: determining whether the first subjective user state occurredat least partially concurrently with occurrence of the first objectiveoccurrence associated with the user.
 32. The computationally-implementedmethod of claim 1, wherein said correlating the subjective user statedata with the objective context data comprises: determining whether thesecond subjective user state occurred at least partially concurrentlywith occurrence of the second objective occurrence associated with theuser.
 33. The computationally-implemented method of claim 1, furthercomprising: presenting one or more results of the correlating.
 34. Thecomputationally-implemented method of claim 33, wherein said presentingone or more results of the correlating comprises: transmitting the oneor more results.
 35. The computationally-implemented method of claim 33,wherein said presenting one or more results of the correlatingcomprises: displaying the one or more results to the user via a userinterface.
 36. The computationally-implemented method of claim 33,wherein said presenting one or more results of the correlatingcomprises: presenting an indication of a sequential relationship betweena subjective user state and an objective occurrence associated with theuser.
 37. The computationally-implemented method of claim 33, whereinsaid presenting one or more results of the correlating comprises:presenting a prediction of a future subjective user state resulting froma future occurrence associated with the user.
 38. Thecomputationally-implemented method of claim 33, wherein said presentingone or more results of the correlating comprises: presenting aprediction of a future subjective user state resulting from a pastoccurrence associated with the user.
 39. The computationally-implementedmethod of claim 33, wherein said presenting one or more results of thecorrelating comprises: presenting a past subjective user stateassociated with a past occurrence associated with the user.
 40. Thecomputationally-implemented method of claim 33, wherein said presentingone or more results of the correlating comprises: presenting arecommendation for a future action.
 41. The computationally-implementedmethod of claim 40, wherein said presenting a recommendation for afuture action comprises: presenting a justification for therecommendation.
 42. The computationally-implemented method of claim 2,wherein said receiving at least a first subjective user state comprises:receiving a first subjective user state from at least one of a wirelessnetwork or a wired network.
 43. The computationally-implemented methodof claim 7, wherein said receiving a first subjective user state thatwas obtained based, at least in part, on data provided by the user, theprovided data indicating the first subjective user state associated withthe user comprises: receiving a first subjective user state that wasobtained based, at least in part, on an audio entry provided by theuser.
 44. The computationally-implemented method of claim 7, whereinsaid receiving a first subjective user state that was obtained based, atleast in part, on data provided by the user, the provided dataindicating the first subjective user state associated with the usercomprises: receiving a first subjective user state that was obtainedbased, at least in part, on an image entry provided by the user.
 45. Acomputationally-implemented system in the form of a machine, article ofmanufacture, or composition of matter, comprising: means for acquiringsubjective user state data including at least a first subjective userstate and a second subjective user state; means for acquiring objectivecontext data including at least a first context data indicative of afirst objective occurrence associated with a user and a second contextdata indicative of a second objective occurrence associated with theuser; and means for correlating the subjective user state data with theobjective context data.